AutoSkill: Experience-Driven Lifelong Learning via Skill Self-Evolution
Yutao Yang, Junsong Li, Qianjun Pan, Bihao Zhan, Yuxuan Cai, Lin Du, Jie Zhou, Kai Chen, Qin Chen, Xin Li, Bo Zhang, Liang He

TL;DR
AutoSkill is a framework that enables large language models to autonomously learn, evolve, and reuse personalized skills from user interactions, enhancing lifelong personalization without retraining.
Contribution
It introduces a model-agnostic plugin system for extracting, maintaining, and dynamically injecting skills from interaction traces, facilitating lifelong learning in LLMs.
Findings
AutoSkill effectively derives skills from dialogue traces.
It supports continual self-evolution of skills.
AutoSkill enables personalized capabilities to be reused across sessions.
Abstract
In practical LLM applications, users repeatedly express stable preferences and requirements, such as reducing hallucinations, following institutional writing conventions, or avoiding overly technical wording, yet such interaction experience is seldom consolidated into reusable knowledge. Consequently, LLM agents often fail to accumulate personalized capabilities across sessions. We present AutoSkill, an experience-driven lifelong learning framework that enables LLM agents to automatically derive, maintain, and reuse skills from dialogue and interaction traces. AutoSkill abstracts skills from user experience, supports their continual self-evolution, and dynamically injects relevant skills into future requests without retraining the underlying model. Designed as a model-agnostic plugin layer, it is compatible with existing LLMs and introduces a standardized skill representation for…
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Taxonomy
TopicsPersonal Information Management and User Behavior · Intelligent Tutoring Systems and Adaptive Learning · AI in Service Interactions
